The terms IoT and Industry 4.0 are promising increasingly sophisticated solutions, but the realisation will depend on the inclusion of robust and reliable sensors. If the gathered data is flawed or inaccurate the performance of the whole system will be compromised. By reviewing research on robustness indicators, mechatronics and sensor properties as well as listing mechanical noise factors and providing an electromechanical trade-off example, the paper highlights the importance of considering both mechanical and electrical noise factors and robustness in early development of connected devices.

The transition to Industry 4.0 poses new challenges for sensor integration design. The foundation of any intelligent system is the data and the data quality depends largely on the integration of the sensor generating it. In this study, the barriers for robust sensor system design are explored through an interview study among practitioners from different industrial contexts. The aim was to explore potential challenges within different contexts and suggest possible directions for research within the field of sensor integration design. It is highlighted that there is a need for multi-disciplinary tools based on synthesis of basic knowledge across relevant engineering domains, like mechanical, electrical and software.

This paper seeks to address the gap between qualitative Robust Design principles and parameter optimization. The former often fails to consider the challenging amount of details in embodiment and configuration design, while the latter is the widely accepted main thrust in traditional Robust Design. The gap is addressed by exploring the value of five quantitative robustness indicators for Design Space Exploration based on variables, objectives and constraints: The set level indicators, Design Space Size and Pareto Set Dispersion, and the point level indicators, Neighbourhood Performance, Failure Rate and Distance to Failure. As a background for the discussion of the limitations of these indicators an industrial case is presented. The case is an incremental encoder and includes two configurations for comparison, five objectives, eight variables, and a range of constraints. The design spaces are sampled and they show conflicting objectives, dispersed spaces and variables dependencies. Based on this it is suggested that set level indicators are more suitable than point level indicators of early robustness evaluation, but the available indicators are limited in their considerations of design space discontinuity and conflicts.